Papers with Code 아카이브

3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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평가표

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보존된 평가 기록

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/tegg89/DLCamp_Jeju2018

Continuous control with deep reinforcement learning

https://github.com/tegg89/magnet

Continuous control with deep reinforcement learning

https://github.com/tegg89/magnn

Continuous control with deep reinforcement learning

https://github.com/tegg89/mann

Continuous control with deep reinforcement learning

https://github.com/tegg89/paper-notes

World Models

https://github.com/tensorflow/models/tree/master/research/deep_contextual_bandits

Parameter Space Noise for Exploration

https://github.com/tensorflow/models/tree/master/research/efficient-hrl

Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

https://github.com/tensorlayer/RLzoo

Continuous control with deep reinforcement learning

https://github.com/thainv0212/re-ddpg

Continuous control with deep reinforcement learning

https://github.com/theSparta/off_policy_mujoco

Off-Policy Deep Reinforcement Learning without Exploration

https://github.com/theresearchai/vehicle_routing_rl_2

Proximal Policy Optimization Algorithms

https://github.com/thomashirtz/pytorch-soft-actor-critic

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/thomashirtz/soft-actor-critic

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/thxsxth/POMDP_RLSepsis

Off-Policy Deep Reinforcement Learning without Exploration

https://github.com/tidedra/vl-rlhf

Proximal Policy Optimization Algorithms

https://github.com/tilkb/thermoai

Continuous control with deep reinforcement learning

https://github.com/timoklein/car_racer

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/tjcdev/mlpgym

Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

https://github.com/tju-drl-lab/self-supervised-rl

What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function Approximator

https://github.com/tkipf/gym-gridworld

CompILE: Compositional Imitation Learning and Execution

https://github.com/tliu1997/rnac

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/tmjeong1103/RL_with_RAY

Proximal Policy Optimization Algorithms

https://github.com/tobiasemrich/SchafkopfRL

Proximal Policy Optimization Algorithms

https://github.com/tomas-gajarsky/facetorch

Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry

https://github.com/tomaszsmaruj25/Twin-Delayed-DDPG-Implementation

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/tommyvsfu1/RL-NTU

Proximal Policy Optimization Algorithms

https://github.com/toni-sm/skrl

Continuous control with deep reinforcement learning

https://github.com/toshikwa/discor.pytorch

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/toshikwa/slac.pytorch

Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

https://github.com/toshikwa/soft-actor-critic.pytorch

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/tphanson/xupr-drl

Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments

https://github.com/trackmania-rl/tmrl

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/tsinghua-rll/gym-gridworld

Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

https://github.com/tuanpnm99/RLPongAgent

Proximal Policy Optimization Algorithms

https://github.com/twni2016/f-IRL

A Divergence Minimization Perspective on Imitation Learning Methods

https://github.com/uber-research/ga-world-models

Deep Neuroevolution of Recurrent and Discrete World Models

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.